{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Softmax exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "This exercise is analogous to the SVM exercise. You will:\n",
    "\n",
    "- implement a fully-vectorized **loss function** for the Softmax classifier\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** with numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3073)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3073)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3073)\n",
      "Test labels shape:  (1000,)\n",
      "dev data shape:  (500, 3073)\n",
      "dev labels shape:  (500,)\n"
     ]
    }
   ],
   "source": [
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n",
    "  \"\"\"\n",
    "  Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "  it for the linear classifier. These are the same steps as we used for the\n",
    "  SVM, but condensed to a single function.  \n",
    "  \"\"\"\n",
    "  # Load the raw CIFAR-10 data\n",
    "  cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "  X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "  \n",
    "  # subsample the data\n",
    "  mask = range(num_training, num_training + num_validation)\n",
    "  X_val = X_train[mask]\n",
    "  y_val = y_train[mask]\n",
    "  mask = range(num_training)\n",
    "  X_train = X_train[mask]\n",
    "  y_train = y_train[mask]\n",
    "  mask = range(num_test)\n",
    "  X_test = X_test[mask]\n",
    "  y_test = y_test[mask]\n",
    "  mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "  X_dev = X_train[mask]\n",
    "  y_dev = y_train[mask]\n",
    "  \n",
    "  # Preprocessing: reshape the image data into rows\n",
    "  X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "  X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "  X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "  X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "  \n",
    "  # Normalize the data: subtract the mean image\n",
    "  mean_image = np.mean(X_train, axis = 0)\n",
    "  X_train -= mean_image\n",
    "  X_val -= mean_image\n",
    "  X_test -= mean_image\n",
    "  X_dev -= mean_image\n",
    "  \n",
    "  # add bias dimension and transform into columns\n",
    "  X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "  X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "  X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "  X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "  \n",
    "  return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = get_CIFAR10_data()\n",
    "print 'Train data shape: ', X_train.shape\n",
    "print 'Train labels shape: ', y_train.shape\n",
    "print 'Validation data shape: ', X_val.shape\n",
    "print 'Validation labels shape: ', y_val.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape\n",
    "print 'dev data shape: ', X_dev.shape\n",
    "print 'dev labels shape: ', y_dev.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Softmax Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/softmax.py**. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from random import shuffle\n",
    "\n",
    "def softmax_loss_naive(W, X, y, reg):\n",
    "  \"\"\"\n",
    "  Softmax loss function, naive implementation (with loops)\n",
    "  Inputs:\n",
    "  - W: D x c array of weights\n",
    "  - X: N x D array of data. Data are D-dimensional columns\n",
    "  - y: 1-dimensional array of length N with labels 0...C-1, for C classes\n",
    "  - reg: (float) regularization strength\n",
    "  Returns:\n",
    "  a tuple of:\n",
    "  - loss as single float\n",
    "  - gradient with respect to weights W, an array of same size as W\n",
    "  \"\"\"\n",
    "  # Initialize the loss and gradient to zero.\n",
    "  data_loss = 0.0\n",
    "  dW = np.zeros_like(W)\n",
    "  n_sample, n_feature  = X.shape\n",
    "  assert n_feature == W.shape[0]\n",
    "  for i in xrange(n_sample):\n",
    "    scores = np.dot(X[i],W)\n",
    "    scores -= np.max(scores)\n",
    "    scores = np.exp(scores)\n",
    "    probability = scores / np.sum(scores)\n",
    "\n",
    "    #accumulate the data_loss\n",
    "    sample_loss = -np.log(probability[y[i]])\n",
    "    data_loss += sample_loss / n_sample\n",
    "\n",
    "    #accumulate the gradient with respect to every single data sample\n",
    "    probability[y[i]] -= 1\n",
    "    dW_single_sample = np.outer(X[i],probability) / n_sample\n",
    "    dW += dW_single_sample\n",
    "\n",
    "  reg_loss = 0.5 * reg * np.sum(W[:-1,:] ** 2)\n",
    "  loss = data_loss + reg_loss\n",
    "\n",
    "  return loss, dW\n",
    "\n",
    "def softmax_loss_vectorized(W, X, y, reg):\n",
    "  \"\"\"\n",
    "  Softmax loss function, vectorized version.\n",
    "  Inputs and outputs are the same as softmax_loss_naive.\n",
    "  \"\"\"\n",
    "  # Initialize the loss and gradient to zero.\n",
    "  dW = np.zeros_like(W)\n",
    "  n_sample = X.shape[0]\n",
    "\n",
    "  assert X.shape[1] == W.shape[0]\n",
    "  scores = np.dot(X,W)\n",
    "\n",
    "  scores -= np.max(scores, axis = 1, keepdims = True)\n",
    "  scores = np.exp(scores)\n",
    "  probs = scores / np.sum(scores, axis = 1, keepdims = True)\n",
    "  kl_loss = np.sum(-np.log(probs[range(n_sample),y])) / n_sample\n",
    "  reg_loss = 0.5 * reg * np.sum(W[:-1,:] ** 2)\n",
    "  loss = kl_loss + reg_loss\n",
    "\n",
    "  dscores = probs\n",
    "  dscores[range(n_sample),y] -= 1\n",
    "  dscores /= n_sample\n",
    "  dW = np.dot(X.T, dscores)\n",
    "\n",
    "  return loss, dW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 2.338973\n",
      "sanity check: 2.302585\n"
     ]
    }
   ],
   "source": [
    "# First implement the naive softmax loss function with nested loops.\n",
    "# Open the file cs231n/classifiers/softmax.py and implement the\n",
    "# softmax_loss_naive function.\n",
    "\n",
    "#from cs231n.classifiers.softmax import softmax_loss_naive\n",
    "import time\n",
    "\n",
    "# Generate a random softmax weight matrix and use it to compute the loss.\n",
    "W = np.random.randn(3073, 10) * 0.0001\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As a rough sanity check, our loss should be something close to -log(0.1).\n",
    "print 'loss: %f' % loss\n",
    "print 'sanity check: %f' % (-np.log(0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1:\n",
    "Why do we expect our loss to be close to -log(0.1)? Explain briefly.**\n",
    "\n",
    "**Your answer:** There are 10 classes in total. After normalization, the probability of the correct class is about 10%, which is 0.1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 4.144013 analytic: 4.144013, relative error: 1.830995e-08\n",
      "numerical: 1.761216 analytic: 1.761216, relative error: 1.672674e-08\n",
      "numerical: 0.080451 analytic: 0.080451, relative error: 1.533439e-07\n",
      "numerical: 0.145145 analytic: 0.145145, relative error: 1.463204e-07\n",
      "numerical: 1.268452 analytic: 1.268451, relative error: 2.772334e-08\n",
      "numerical: -0.060458 analytic: -0.060458, relative error: 4.667356e-07\n",
      "numerical: 0.101639 analytic: 0.101639, relative error: 1.498645e-07\n",
      "numerical: -0.695755 analytic: -0.695755, relative error: 7.948496e-09\n",
      "numerical: -1.809145 analytic: -1.809145, relative error: 2.499556e-08\n",
      "numerical: -0.179706 analytic: -0.179706, relative error: 4.333396e-07\n",
      "numerical: 0.443660 analytic: 0.435165, relative error: 9.666078e-03\n",
      "numerical: -0.128323 analytic: -0.137962, relative error: 3.619774e-02\n",
      "numerical: 0.375964 analytic: 0.365794, relative error: 1.371018e-02\n",
      "numerical: 0.983938 analytic: 0.978784, relative error: 2.626056e-03\n",
      "numerical: 0.053598 analytic: 0.050033, relative error: 3.439851e-02\n",
      "numerical: -1.711375 analytic: -1.715959, relative error: 1.337574e-03\n",
      "numerical: -0.261998 analytic: -0.262100, relative error: 1.941497e-04\n",
      "numerical: -1.378566 analytic: -1.378030, relative error: 1.942954e-04\n",
      "numerical: -1.202483 analytic: -1.211551, relative error: 3.756042e-03\n",
      "numerical: -0.465997 analytic: -0.470988, relative error: 5.327327e-03\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of softmax_loss_naive and implement a (naive)\n",
    "# version of the gradient that uses nested loops.\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# As we did for the SVM, use numeric gradient checking as a debugging tool.\n",
    "# The numeric gradient should be close to the analytic gradient.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)\n",
    "\n",
    "# similar to SVM case, do another gradient check with regularization\n",
    "loss, grad = softmax_loss_naive(W, X_dev, y_dev, 1e2)\n",
    "f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naive loss: 2.338973e+00 computed in 0.199303s\n",
      "vectorized loss: 2.338973e+00 computed in 0.036854s\n",
      "Loss difference: 0.000000\n",
      "Gradient difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Now that we have a naive implementation of the softmax loss function and its gradient,\n",
    "# implement a vectorized version in softmax_loss_vectorized.\n",
    "# The two versions should compute the same results, but the vectorized version should be\n",
    "# much faster.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'naive loss: %e computed in %fs' % (loss_naive, toc - tic)\n",
    "\n",
    "from cs231n.classifiers.softmax import softmax_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)\n",
    "\n",
    "# As we did for the SVM, we use the Frobenius norm to compare the two versions\n",
    "# of the gradient.\n",
    "grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print 'Loss difference: %f' % np.abs(loss_naive - loss_vectorized)\n",
    "print 'Gradient difference: %f' % grad_difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1e-07, 50000.0)\n",
      "validation accuracy: 0.252000\n",
      "(1e-07, 100000000.0)\n",
      "validation accuracy: 0.242000\n",
      "(5e-07, 50000.0)\n",
      "validation accuracy: 0.313000\n",
      "(5e-07, 100000000.0)\n",
      "validation accuracy: 0.335000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.249776 val accuracy: 0.252000\n",
      "lr 1.000000e-07 reg 1.000000e+08 train accuracy: 0.239776 val accuracy: 0.242000\n",
      "lr 5.000000e-07 reg 5.000000e+04 train accuracy: 0.316755 val accuracy: 0.313000\n",
      "lr 5.000000e-07 reg 1.000000e+08 train accuracy: 0.314531 val accuracy: 0.335000\n",
      "best validation accuracy achieved during cross-validation: 0.335000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of over 0.35 on the validation set.\n",
    "from cs231n.classifiers import Softmax\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_softmax = None\n",
    "learning_rates = [1e-7, 5e-7]\n",
    "regularization_strengths = [5e4, 1e8]\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained softmax classifer in best_softmax.                          #\n",
    "################################################################################\n",
    "params = [(x,y) for x in learning_rates for y in regularization_strengths]\n",
    "\n",
    "for (lr, reg) in params:\n",
    "    print(lr, reg)\n",
    "    softmax = Softmax()\n",
    "    softmax.train(X_train,y_train,learning_rate = lr,reg = reg,num_iters = 1500,batch_size = 200,verbose = False)\n",
    "\n",
    "    train_y_predict = softmax.predict(X_train)\n",
    "    valid_y_predict = softmax.predict(X_val)\n",
    "    train_accuracy = np.mean(y_train == train_y_predict)\n",
    "    val_accuracy = np.mean(y_val == valid_y_predict)\n",
    "    results[(lr,reg)] = (train_accuracy,val_accuracy)\n",
    "    if val_accuracy > best_val:\n",
    "        best_val = val_accuracy\n",
    "        best_softmax = softmax  \n",
    "\n",
    "    print 'validation accuracy: %f' % (val_accuracy, )\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy)\n",
    "    \n",
    "print 'best validation accuracy achieved during cross-validation: %f' % best_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "softmax on raw pixels final test set accuracy: 0.285000\n"
     ]
    }
   ],
   "source": [
    "# evaluate on test set\n",
    "# Evaluate the best softmax on test set\n",
    "y_test_pred = best_softmax.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print 'softmax on raw pixels final test set accuracy: %f' % (test_accuracy, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y23HWhCymbyoO4sITEemNZazW525X8fz19JuRhdvrx5N8R0oU9ewsn5oQ0xqY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6b6e9b150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class\n",
    "w = best_softmax.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in xrange(10):\n",
    "  plt.subplot(2, 5, i + 1)\n",
    "  \n",
    "  # Rescale the weights to be between 0 and 255\n",
    "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "  plt.imshow(wimg.astype('uint8'))\n",
    "  plt.axis('off')\n",
    "  plt.title(classes[i])"
   ]
  }
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